The utilization of patients’ information to improve the performance of radiotherapy centers: A data-driven approach

The high demand for radiotherapy services, combined with the limited capacity of available resources, patient unpunctuality, and series of appointments, makes Patient Appointment Scheduling (PAS) in radiotherapy centers very challenging. Although most centers use a First-Come-First-Serve (FCFS) policy for appointment scheduling, this approach does not consider patients' behaviors, and consequently, it performs poorly. This type of inappropriate scheduling usually leads to inefficiency at the center and/or patient dissatisfaction. This study provides a data-driven approach to patient appointment scheduling to deal with the challenges mentioned above, and it considers patients' histories of unpunctuality, including the amount of time they are usually late and whether they will miss the appointment. This study first employs data-mining techniques to predict patients' behaviors and then incorporates them into PAS. In addition, it presents a novel double-stage prioritization method that considers both patients' gradual health improvement during the treatment process and any treatment prolongation that occurs. These predictions and priorities are then utilized in the developed Mixed Integer Linear Programming (MILP) model to determine the optimal sequence of patients for treatment. The developed model also considers no-show patients and rearranges their makeup session(s) to meet their service requirements. Lastly, the proposed approach is applied to two business configurations (i.e., single-server and multi-server radiotherapy centers) to highlight its advantages and demonstrate its performance against the current policy. The results reveal that employing the developed model improves the center's total cost by up to 30%.